Snowball: extracting relations from large plain-text collections
DL '00 Proceedings of the fifth ACM conference on Digital libraries
The Journal of Machine Learning Research
Discovering relations among named entities from large corpora
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Espresso: leveraging generic patterns for automatically harvesting semantic relations
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Clustering for unsupervised relation identification
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Introduction to Information Retrieval
Introduction to Information Retrieval
A comparison of extrinsic clustering evaluation metrics based on formal constraints
Information Retrieval
TextRunner: open information extraction on the web
NAACL-Demonstrations '07 Proceedings of Human Language Technologies: The Annual Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations
Unsupervised methods for determining object and relation synonyms on the web
Journal of Artificial Intelligence Research
The nested chinese restaurant process and bayesian nonparametric inference of topic hierarchies
Journal of the ACM (JACM)
FAM-LbR '10 Proceedings of the NAACL HLT 2010 First International Workshop on Formalisms and Methodology for Learning by Reading
Unsupervised relation discovery with sense disambiguation
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Structural linguistics and unsupervised information extraction
AKBC-WEKEX '12 Proceedings of the Joint Workshop on Automatic Knowledge Base Construction and Web-scale Knowledge Extraction
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This paper presents a generative model for the automatic discovery of relations between entities in electronic medical records. The model discovers relation instances and their types by determining which context tokens express the relation. Additionally, the valid semantic classes for each type of relation are determined. We show that the model produces clusters of relation trigger words which better correspond with manually annotated relations than several existing clustering techniques. The discovered relations reveal some of the implicit semantic structure present in patient records.